NNguyen_NationsCharts

Chart 1

library(tidyverse)

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library(ggplot2)
library(RColorBrewer)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

getwd()

[1] "/Users/natty/Downloads/DATA110"

setwd("/Users/natty/Downloads/DATA110")
nations <- read_csv("nations.csv")

Rows: 5275 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): iso2c, iso3c, country, region, income
dbl (5): year, gdp_percap, population, birth_rate, neonat_mortal_rate

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

largestFour <- nations |>
  mutate(gdp = gdp_percap * population / 10^12) |>
  filter(country == "China" | country == "Germany" | country == "Japan"| country == "United States")

chart1 <- largestFour |>
ggplot(aes(x = year, y = gdp, color = country)) +
scale_color_brewer(palette = "Set1") +
ylab("GDP ($ trillion)") +
theme_minimal(base_size = 12) +
geom_point() +
geom_line() +
xlim(1990,2014) +
ggtitle("China's Rise to Become the Largest Economy")
chart1

Chart 2

nations_area <- nations |>
  mutate(gdp = gdp_percap * population / 10^12) |>
  group_by(region, year) |>
  summarise(sum = sum(gdp, na.rm = TRUE))

`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.

chart2 <- nations_area |>
ggplot(aes(x = year, y = sum, fill = region)) +
scale_fill_brewer(palette = "Set2") +
ylab("GDP ($ trillion)") +
theme_minimal(base_size = 12) +
geom_area(color = "white") +
ggtitle("GDP by World Bank Region")
chart2